Examples of using Multivariate data in English and their translations into Spanish
{-}
-
Official
-
Colloquial
RUM offers first multivariate data analysis course.
Description Graphic representation of multivariate data.
Which multivariate data analysis method to choose?
Palabras clave: UV-visible spectrometer, water quality, multivariate data.
Choosing an appropriate multivariate data analysis technique.
Multivariate data analysis was performed using logistic regression.
Chapter 6: Visualization of multivariate data Dimensionality reduction.
This approach was welcomed as the first attempt to draw together in a formalised fashion the multivariate data from the CEMP program.
RUM offers first multivariate data analysis course| Ciencia Puerto Rico.
His team also performed principal component analyses,which is good at analysing multivariate data with minimal loss of information.
It provides a multivariate data order based on a concept that we will call“extremality”.
Market research Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels.
Easily analyze and interpret multivariate data and its relationships more completely.
The objective of PCA is to achieve parsimony andreduce dimensionality by extracting the smallest number components that account for most of the variation in the original multivariate data and to summarize the data with little loss of information.
In contrast, the component-wise median for a multivariate data set is not in general rotation invariant, nor is it independent of the choice of coordinates.
See e.g., Moore, David S. and McCabe, George P., Introduction to the Practice of Statistics(W. H. Freeman and Company, New York, 1989); Pindyck, Robert S. and Rubinfeld, Daniel L., Econometric Models and Econometric Forecasts(3rd ed.)(McGraw-Hill, Inc., New York, 1991); Jobson, J. D.,Applied Multivariate Data Analysis, Vol.
This dataset will be used to estimate the centering matrix for the multivariate data and the variances to be used in transforming the data into an approximately standard multinormal distribution.
When performing non-selective measurements,a sum signal from several analytes is measured which means that multivariate data analyses such as neural networks have to be used for quantification.
Since the issues are complex,we have been compelled to include some(seemingly) complex multivariate data analysis in order to justify our conclusions, most of the key findings are conveyed with graphs that we hope most readers will readily comprehend.
The book by Gower, Lubbe and le Roux(2011) aims to popularize biplots as a useful andreliable method for the visualization of multivariate data when researchers want to consider, for example, principal component analysis(PCA), canonical variates analysis(CVA) or various types of correspondence analysis.
Chemometric modelling of multivariate spectroscopic data Summary.
For the analysis of data three multivariate methods were used.
Latent class analysis(LCA) is a subset of structural equation modeling, used to find groups orsubtypes of cases in multivariate categorical data.
Other countries indicated the need to collect a broader array of data for multivariate analysis on a large number of interconnected topics.